Bagging support vector data description model for batch process monitoring

作     者：Ge, Zhiqiang Song, Zhihuan 

作者机构：Zhejiang Univ State Key Lab Ind Control Technol Inst Ind Proc Control Dept Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China 

出 版 物：《JOURNAL OF PROCESS CONTROL》 (工艺过程控制杂志)

年 卷 期：2013年第23卷第8期

页      面：1090-1096页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 0817[工学-化学工程与技术] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0701[理学-数学] 071101[理学-系统理论] 

基　　金：National Natural Science Foundation of China (NSFC) National Project 973 [2012CB720500] Fundamental Research Funds for the Central Universities [2013QNA5016] 

主　　题：Batch process monitoring Support vector data description Ensemble learning Bagging Bayesian combination 

摘      要：To improve the monitoring performance of the support vector data description model (SVDD), an ensemble form of SVDD is developed, which is termed as bagging SVDD in this paper. While different kinds of ensemble learning approaches have been developed in the past years, bagging is probably the most traditional and simplest one. By randomly selecting subsets from the original dataset, bagging constructs an individual SVDD model for each of these subsets. For practical utilization, the results of different individual SVDD models are ensembled/combined together. In this paper, two kinds of combination strategies are proposed, named as voting-based strategy and Bayesian-based strategy. Compared to a single SVDD model, the monitoring performance can be improved by the bagging SVDD method in most cases. The feasibility and effectiveness of the proposed method are demonstrated by an industrial semiconductor etch process. (C) 2013 Elsevier Ltd. All rights reserved.